Capturing Deviations From Design Intent in Building Simulation Models for Risk Assessment

Author(s):  
Heikki Nikula ◽  
Seppo Sierla ◽  
Bryan O'Halloran ◽  
Tommi Karhela

Simulation-based methods are emerging to address the challenges of complex systems risk assessment, and this paper identifies two problems related to the use of such methods. First, the methods cannot identify new hazards if the simulation model builders are expected to foresee the hazards and incorporate the abnormal behavior related to the hazard into the simulation model. Therefore, this paper uses the concept of deviation from design intent to systematically capture abnormal conditions that may lead to component failures, hazards, or both. Second, simulation-based risk assessment methods should explicitly consider what expertise is required from the experts that build and use the simulation models—the transfer of the methods to real engineering practice will be severely hindered if they must be performed by persons that are expert in domain safety as well as advanced computer simulation-based methods. This paper addresses both problems in the context of the functional failure identification and propagation (FFIP) method. One industrially established risk assessment method, hazard and operability study (HAZOP), is harnessed to systematically obtain the deviations from design intent in the application under study. An information system presents a user interface that is understandable to HAZOP professionals, so that their inputs are transparently entered to a data model that captures the deviations. From the data model, instructions for configuring FFIP simulation models are printed in a form that is understandable for FFIP experts. The method is demonstrated for discovering a hazard resulting from system-wide fault propagation in a boiling water reactor case.

2020 ◽  
Vol 34 (5) ◽  
pp. 627-640 ◽  
Author(s):  
Shi Xianwu ◽  
Qiu Jufei ◽  
Chen Bingrui ◽  
Zhang Xiaojie ◽  
Guo Haoshuang ◽  
...  

Author(s):  
Zuzhen Ji ◽  
Dirk Pons ◽  
John Pearse

Successful implementation of Health and Safety (H&S) systems requires an effective mechanism to assess risk. Existing methods focus primarily on measuring the safety aspect; the risk of an accident is determined based on the product of severity of consequence and likelihood of the incident arising. The health component, i.e., chronic harm, is more difficult to assess. Partially, this is due to both consequences and the likelihood of health issues, which may be indeterminate. There is a need to develop a quantitative risk measurement for H&S risk management and with better representation for chronic health issues. The present paper has approached this from a different direction, by adopting a public health perspective of quality of life. We have then changed the risk assessment process to accommodate this. This was then applied to a case study. The case study showed that merely including the chronic harm scales appeared to be sufficient to elicit a more detailed consideration of hazards for chronic harm. This suggests that people are not insensitive to chronic harm hazards, but benefit from having a framework in which to communicate them. A method has been devised to harmonize safety and harm risk assessments. The result was a comprehensive risk assessment method with consideration of safety accidents and chronic health issues. This has the potential to benefit industry by making chronic harm more visible and hence more preventable.


2021 ◽  
Vol 420 ◽  
pp. 129893
Author(s):  
Zijian Liu ◽  
Wende Tian ◽  
Zhe Cui ◽  
Honglong Wei ◽  
Chuankun Li

2021 ◽  
Vol 102 ◽  
pp. 102134
Author(s):  
Junjiang He ◽  
Tao Li ◽  
Beibei Li ◽  
Xiaolong Lan ◽  
Zhiyong Li ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hamid Reza Marateb ◽  
Maja von Cube ◽  
Ramin Sami ◽  
Shaghayegh Haghjooy Javanmard ◽  
Marjan Mansourian ◽  
...  

Abstract Background Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]). Conclusions This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.


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